Hidden Markov Model based channel selection framework for cognitive radio network. (January 2018)

• Record Type: Journal Article
• Authors: S., Senthilkumar; C., Geetha Priya
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 516
• Page End: 526
• Publication Date: 2018-01
• Subjects: Cognitive radio network (CRN) -- Channel selection -- Hidden Markov Model (HMM) -- Rayleigh channel -- Time-slot based optimal routing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.06.016
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The Hidden Markov Model (HMM) selects the optimal channel for the users. The Time-Slot based routing mechanism optimized the spectrum-band range. The transmission speed is increased by selecting the optimal channel. Also, the packet loss and bandwidth loss are reduced. Abstract: Due to the effective utilization rate of the radio frequency spectrum, Cognitive Radio Network (CRN) has gained more popularity in the current research field. The spectrum sensing techniques detect the presence of the idle channel and reallocate to the Secondary Users (SUs). However, the existing spectrum sensing and channel estimation approaches incurs delay while searching for the new channels. To reduce the delay and achieve optimal selection of the channel in CRN, this paper proposes a Hidden Markov Model (HMM)-based channel selection framework. The Time-Slot based optimal routing mechanism is introduced to minimize the delay occurred during the channel search and optimize the range of the spectrum band. Therefore, the bandwidth range of the node is estimated, and the channel is allocated to the SU. The proposed framework exhibit better end-to-end throughput, bandwidth-power product and lower running time, energy consumption, and average end-to-end delay when compared to the existing schemes. Graphical abstract: